Wai Kin (Victor) Chan

Rensselaer Polytechnic InstituteUnited States of America

Dr. Wai Kin (Victor) Chan is an Associate Professor of the Department of Industrial and Systems Engineering at the Rensselaer Polytechnic Institute (RPI), Troy, NY. He received a Ph.D. degree in industrial engineering and operations research from the University of California, Berkeley in 2005. Dr. Chan has published papers in Operations Research, ACM TOMACS, INFORMS Journal of Service Science, IEEE Transactions, International Journal of Production Research, and Artificial Intelligence Review. Dr. Chan is a recipient of the Pritsker thesis award, the NSF CAREER Award, the IEEE CASE Best Paper Awards, and the INFORMS Service Science Best Paper Award. Dr. Chan serves and has served as a program co-chair of the 2013 Industrial and System Engineering Research Conference, editor of the IEEE Conference on Automation Science and Engineering, associate editor of the IIE Transactions, and track chair of the Winter Simulation Conference. Dr. Chan is a member of INFORMS, IIE, IEEE, and Alpha Pi Mu.

1books edited

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Latest work with IntechOpen by Wai Kin (Victor) Chan

The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique. The 11 chapters of this book collectively illustrates how such a sampling technique is exploited to solve difficult problems or analyze complex systems in various engineering and science domains. Issues related to the use of MCS including goodness-of-fit, uncertainty evaluation, variance reduction, optimization, and statistical estimation are discussed and examples of solutions are given. Novel applications of MCS are demonstrated in financial systems modeling, estimation of transition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. To enlarge the accessibility of this book, both field-specific background materials and field-specific usages of MCS are introduced in most chapters. The aim of this book is to unify knowledge of MCS from different fields to facilitate research and new applications of MCS.

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